{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from math import *\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 멤리스터"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Memristance\n",
    "- Roff = 250kohm, Coff=4e-06\n",
    "- Ron = 2.5kohm, Con=4e-04\n",
    "### Changed by dx/dt = g(V(t))*f(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "interval = 0.01 # The base time of the code(구분구적법을 수행할 때 필요함)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3x3 memristor crossbar\n",
    "# 전압으로 MEMRISTOR 저\n",
    "class Memristor:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        self.x = 0.1\n",
    "        self.xp = 0.1\n",
    "        self.xn = 0.2\n",
    "        self.vp = 1.5\n",
    "        self.vn = 0.7\n",
    "        self.alphap = 6\n",
    "        self.alphan = 4\n",
    "        self.a1 = 4e-4\n",
    "        self.a2 = 3e-4\n",
    "        self.ap = 0.032\n",
    "        self.an = 0.001\n",
    "        self.b = 1.0\n",
    "        \n",
    "        self.gf = 0.0\n",
    "        \n",
    "        self.interval = interval\n",
    "        self.conductance = 0\n",
    "        self.resistance = 0\n",
    "    \n",
    "    def change_x(self, V):\n",
    "        if V >= 0:\n",
    "            if self.x >= self.xp:\n",
    "                self.f = exp(-self.alphap*(self.x - self.xp)) * (((self.xp - self.x)/(1 - self.xp)) + 1)\n",
    "            else:\n",
    "                self.f = 1.0\n",
    "        elif V < 0:\n",
    "            if self.x <= (1 - self.xn):\n",
    "                self.f = exp(self.alphan*(self.x + self.xn - 1.0)) * (self.x/(1-self.xn))\n",
    "            else:\n",
    "                self.f = 1.0\n",
    "            \n",
    "        if V > self.vp:\n",
    "            self.g = self.ap * (exp(V)-exp(self.vp))\n",
    "        elif V < -self.vn:\n",
    "            self.g = -self.an * (exp(-V)-exp(self.vn))\n",
    "        else:\n",
    "            self.g = 0\n",
    "            \n",
    "        self.gf = self.f * self.g\n",
    "        self.x += self.gf * self.interval\n",
    "    \n",
    "    def activate(self, V):\n",
    "        self.change_x(V)\n",
    "        \n",
    "        if V >= 0:\n",
    "            curr = self.a1 * self.x * sinh(self.b * V)\n",
    "        else:\n",
    "            curr = self.a2 * self.x * sinh(self.b * V)\n",
    "        \n",
    "        try: self.resistance = V / curr\n",
    "        except ZeroDivisionError as e:\n",
    "            print(e)\n",
    "            \n",
    "        try: self.conductance = 1 / self.resistance\n",
    "        except ZeroDivisionError as e:\n",
    "            print(e)\n",
    "            \n",
    "        return curr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "mem_1 = Memristor('mem_1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_time = np.arange(0.1, 30*pi, mem_1.interval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_volt = []\n",
    "for t in base_time:\n",
    "    input_volt.append(4.0 * sin(t/15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa0b50c1390>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0b47e6160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(base_time, input_volt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = []\n",
    "for V in input_volt:\n",
    "    curr = mem_1.activate(V)\n",
    "    output.append(curr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = np.array(output)\n",
    "output = output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Voltage(V)')"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0b445d438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.grid()\n",
    "plt.scatter(input_volt, output, s=0.02)\n",
    "plt.ylabel('Current(A)')\n",
    "plt.xlabel('Voltage(V)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For presentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mem_2 = Memristor(\"mem_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa082125518>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0821975c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output_2 = []\n",
    "for V in input_volt:\n",
    "    mem_2.activate(V)\n",
    "    output_2.append(mem_2.f)\n",
    "    \n",
    "output_2 = np.array(output_2)\n",
    "plt.scatter(input_volt, output_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa081ecbdd8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa081f49208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_volt_2 = np.arange(-5.0, 5.0, interval)\n",
    "\n",
    "def function_g(V):\n",
    "    if V > 1.5:\n",
    "            g = 0.032 * (exp(V)-exp(1.5))\n",
    "    elif V < -0.7:\n",
    "        g = -0.001 * (exp(-V)-exp(0.7))\n",
    "    else:\n",
    "        g = 0\n",
    "    return g\n",
    "\n",
    "output = []\n",
    "for V in input_volt_2:\n",
    "    output.append(function_g(V))\n",
    "\n",
    "plt.rcParams.update({'font.size': 22})\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlabel('Voltage')\n",
    "plt.ylabel('function g')\n",
    "plt.grid()\n",
    "plt.scatter(input_volt_2, output, s=2.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "self.x = 0.1\n",
    "        self.xp = 0.1\n",
    "        self.xn = 0.2\n",
    "        self.vp = 1.5\n",
    "        self.vn = 0.7\n",
    "        self.alphap = 6\n",
    "        self.alphan = 4\n",
    "        self.a1 = 4e-4\n",
    "        self.a2 = 3e-4\n",
    "        self.ap = 0.032\n",
    "        self.an = 0.001\n",
    "        self.b = 1.0\n",
    "        \n",
    "        self.gf = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7fa0818d84e0>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0818ac0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "input_x = np.arange(0.0, 1.0, interval/2)\n",
    "V=1\n",
    "\n",
    "def function_f(x):\n",
    "    if V >= 0:\n",
    "        if x >= 0.1:\n",
    "            f = exp(-6*(x - 0.1)) * (((0.1 - x)/(1 - 0.1)) + 1)\n",
    "        else:\n",
    "            f = 1.0\n",
    "    elif V < 0:\n",
    "        if x <= (1 - 0.2):\n",
    "            f = exp(4*(x + 0.2 - 1.0)) * (x/(1-0.2))\n",
    "        else:\n",
    "            f = 1.0\n",
    "    return f\n",
    "            \n",
    "output_2 = []\n",
    "for x in input_x:\n",
    "    output_2.append(function_f(x))\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "plt.xlabel('x')\n",
    "plt.xlim([0.0,1.0])\n",
    "plt.ylabel('function f')\n",
    "plt.grid()\n",
    "plt.scatter(input_x, output_2, s=8.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resistor calculator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def resistcalc(x):\n",
    "    return 2.5e3*x + 250e3*(1-x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resistcalc(1)/10**3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Memristor animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.animation as animation\n",
    "from IPython.display import HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.06678519e-06,  1.17349108e-06,  1.28020480e-06, ...,\n",
       "       -1.02981957e-06, -6.59105610e-07, -2.88396044e-07])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0b4676710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mem_2 = Memristor('mem_2')\n",
    "# fig = plt.figure(figsize=(16,8))\n",
    "# ax = plt.subplot(1,1,1)\n",
    "# ax.set_xlim([-5,5])\n",
    "# ax.set_ylim([-0.006,0.01])\n",
    "\n",
    "fig = plt.figure(figsize=(16,8))\n",
    "ax = plt.subplot(1,1,1)\n",
    "\n",
    "def voltage_generator():\n",
    "    for a in range(0,len(input_volt),100):\n",
    "         yield list(zip(input_volt, output))[a]\n",
    "\n",
    "output_list = []\n",
    "base_volt = []\n",
    "def update(data):\n",
    "    output_list.append(data[1])\n",
    "    base_volt.append(data[0])\n",
    "    ax.clear()\n",
    "    ax.set_ylabel('Current(A)')\n",
    "    ax.set_xlabel('Voltage(V)')\n",
    "    ax.set_xlim([-5,5])\n",
    "    ax.set_ylim([-0.006,0.01])\n",
    "    ax.scatter(base_volt, output_list)\n",
    "    return ln,\n",
    "\n",
    "\n",
    "anim = animation.FuncAnimation(fig, update, voltage_generator, interval=10, repeat=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "Writer = animation.writers['ffmpeg']\n",
    "writer = Writer(fps=9, metadata=dict(artist='Me'), bitrate=1800)\n",
    "\n",
    "anim.save('../animation/memristor_model.mp4', writer=writer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
